Overview

Dataset statistics

Number of variables18
Number of observations891
Missing cells179
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory125.4 KiB
Average record size in memory144.1 B

Variable types

Numeric7
Categorical7
Text4

Alerts

Family_size is highly overall correlated with Family_type and 3 other fieldsHigh correlation
Family_type is highly overall correlated with Family_size and 2 other fieldsHigh correlation
Fare is highly overall correlated with Family_size and 1 other fieldsHigh correlation
Parch is highly overall correlated with Family_size and 1 other fieldsHigh correlation
Pclass is highly overall correlated with deckHigh correlation
Sex is highly overall correlated with Survived and 1 other fieldsHigh correlation
SibSp is highly overall correlated with Family_size and 1 other fieldsHigh correlation
Survived is highly overall correlated with Sex and 1 other fieldsHigh correlation
deck is highly overall correlated with PclassHigh correlation
ind_fare is highly overall correlated with FareHigh correlation
title is highly overall correlated with Sex and 1 other fieldsHigh correlation
title is highly imbalanced (52.8%)Imbalance
deck is highly imbalanced (57.2%)Imbalance
Age has 177 (19.9%) missing valuesMissing
PassengerId is uniformly distributedUniform
PassengerId has unique valuesUnique
Name has unique valuesUnique
SibSp has 608 (68.2%) zerosZeros
Parch has 678 (76.1%) zerosZeros
Fare has 15 (1.7%) zerosZeros
ind_fare has 15 (1.7%) zerosZeros

Reproduction

Analysis started2026-01-23 17:41:23.035163
Analysis finished2026-01-23 17:41:30.575040
Duration7.54 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ)

Uniform  Unique 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446
Minimum1
Maximum891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-23T23:11:30.759138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45.5
Q1223.5
median446
Q3668.5
95-th percentile846.5
Maximum891
Range890
Interquartile range (IQR)445

Descriptive statistics

Standard deviation257.35384
Coefficient of variation (CV)0.57702655
Kurtosis-1.2
Mean446
Median Absolute Deviation (MAD)223
Skewness0
Sum397386
Variance66231
MonotonicityStrictly increasing
2026-01-23T23:11:31.056348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8911
 
0.1%
11
 
0.1%
21
 
0.1%
31
 
0.1%
41
 
0.1%
51
 
0.1%
61
 
0.1%
71
 
0.1%
81
 
0.1%
91
 
0.1%
Other values (881)881
98.9%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
8911
0.1%
8901
0.1%
8891
0.1%
8881
0.1%
8871
0.1%
8861
0.1%
8851
0.1%
8841
0.1%
8831
0.1%
8821
0.1%

Survived
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
0
549 
1
342 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0549
61.6%
1342
38.4%

Length

2026-01-23T23:11:31.198634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-23T23:11:31.286982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0549
61.6%
1342
38.4%

Most occurring characters

ValueCountFrequency (%)
0549
61.6%
1342
38.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0549
61.6%
1342
38.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0549
61.6%
1342
38.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0549
61.6%
1342
38.4%

Pclass
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
3
491 
1
216 
2
184 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3491
55.1%
1216
24.2%
2184
 
20.7%

Length

2026-01-23T23:11:31.361153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-23T23:11:31.431984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3491
55.1%
1216
24.2%
2184
 
20.7%

Most occurring characters

ValueCountFrequency (%)
3491
55.1%
1216
24.2%
2184
 
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3491
55.1%
1216
24.2%
2184
 
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3491
55.1%
1216
24.2%
2184
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3491
55.1%
1216
24.2%
2184
 
20.7%

Name
Text

Unique 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2026-01-23T23:11:31.642137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length82
Median length52
Mean length26.965208
Min length12

Characters and Unicode

Total characters24026
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique891 ?
Unique (%)100.0%

Sample

1st rowBraund, Mr. Owen Harris
2nd rowCumings, Mrs. John Bradley (Florence Briggs Thayer)
3rd rowHeikkinen, Miss. Laina
4th rowFutrelle, Mrs. Jacques Heath (Lily May Peel)
5th rowAllen, Mr. William Henry
ValueCountFrequency (%)
mr521
 
14.4%
miss182
 
5.0%
mrs129
 
3.6%
william64
 
1.8%
john44
 
1.2%
master40
 
1.1%
henry35
 
1.0%
james24
 
0.7%
george24
 
0.7%
charles23
 
0.6%
Other values (1515)2538
70.0%
2026-01-23T23:11:32.061412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2735
 
11.4%
r1958
 
8.1%
e1703
 
7.1%
a1657
 
6.9%
i1325
 
5.5%
n1304
 
5.4%
s1297
 
5.4%
M1128
 
4.7%
l1067
 
4.4%
o1008
 
4.2%
Other values (50)8844
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)24026
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r1958
 
8.1%
e1703
 
7.1%
a1657
 
6.9%
i1325
 
5.5%
n1304
 
5.4%
s1297
 
5.4%
M1128
 
4.7%
l1067
 
4.4%
o1008
 
4.2%
Other values (50)8844
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24026
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r1958
 
8.1%
e1703
 
7.1%
a1657
 
6.9%
i1325
 
5.5%
n1304
 
5.4%
s1297
 
5.4%
M1128
 
4.7%
l1067
 
4.4%
o1008
 
4.2%
Other values (50)8844
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24026
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r1958
 
8.1%
e1703
 
7.1%
a1657
 
6.9%
i1325
 
5.5%
n1304
 
5.4%
s1297
 
5.4%
M1128
 
4.7%
l1067
 
4.4%
o1008
 
4.2%
Other values (50)8844
36.8%

Sex
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
male
577 
female
314 

Length

Max length6
Median length4
Mean length4.704826
Min length4

Characters and Unicode

Total characters4192
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male577
64.8%
female314
35.2%

Length

2026-01-23T23:11:32.159930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-23T23:11:32.228259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male577
64.8%
female314
35.2%

Most occurring characters

ValueCountFrequency (%)
e1205
28.7%
m891
21.3%
a891
21.3%
l891
21.3%
f314
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)4192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1205
28.7%
m891
21.3%
a891
21.3%
l891
21.3%
f314
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1205
28.7%
m891
21.3%
a891
21.3%
l891
21.3%
f314
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1205
28.7%
m891
21.3%
a891
21.3%
l891
21.3%
f314
 
7.5%

Age
Real number (ℝ)

Missing 

Distinct88
Distinct (%)12.3%
Missing177
Missing (%)19.9%
Infinite0
Infinite (%)0.0%
Mean29.699118
Minimum0.42
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-23T23:11:32.325477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile4
Q120.125
median28
Q338
95-th percentile56
Maximum80
Range79.58
Interquartile range (IQR)17.875

Descriptive statistics

Standard deviation14.526497
Coefficient of variation (CV)0.48912219
Kurtosis0.17827415
Mean29.699118
Median Absolute Deviation (MAD)9
Skewness0.38910778
Sum21205.17
Variance211.01912
MonotonicityNot monotonic
2026-01-23T23:11:32.535194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2430
 
3.4%
2227
 
3.0%
1826
 
2.9%
2825
 
2.8%
3025
 
2.8%
1925
 
2.8%
2124
 
2.7%
2523
 
2.6%
3622
 
2.5%
2920
 
2.2%
Other values (78)467
52.4%
(Missing)177
 
19.9%
ValueCountFrequency (%)
0.421
 
0.1%
0.671
 
0.1%
0.752
 
0.2%
0.832
 
0.2%
0.921
 
0.1%
17
0.8%
210
1.1%
36
0.7%
410
1.1%
54
 
0.4%
ValueCountFrequency (%)
801
 
0.1%
741
 
0.1%
712
0.2%
70.51
 
0.1%
702
0.2%
661
 
0.1%
653
0.3%
642
0.2%
632
0.2%
624
0.4%

SibSp
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52300786
Minimum0
Maximum8
Zeros608
Zeros (%)68.2%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-23T23:11:32.641894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1027434
Coefficient of variation (CV)2.1084644
Kurtosis17.88042
Mean0.52300786
Median Absolute Deviation (MAD)0
Skewness3.6953517
Sum466
Variance1.2160431
MonotonicityNot monotonic
2026-01-23T23:11:32.717037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0608
68.2%
1209
 
23.5%
228
 
3.1%
418
 
2.0%
316
 
1.8%
87
 
0.8%
55
 
0.6%
ValueCountFrequency (%)
0608
68.2%
1209
 
23.5%
228
 
3.1%
316
 
1.8%
418
 
2.0%
55
 
0.6%
87
 
0.8%
ValueCountFrequency (%)
87
 
0.8%
55
 
0.6%
418
 
2.0%
316
 
1.8%
228
 
3.1%
1209
 
23.5%
0608
68.2%

Parch
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38159371
Minimum0
Maximum6
Zeros678
Zeros (%)76.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-23T23:11:32.786945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80605722
Coefficient of variation (CV)2.1123441
Kurtosis9.7781252
Mean0.38159371
Median Absolute Deviation (MAD)0
Skewness2.749117
Sum340
Variance0.64972824
MonotonicityNot monotonic
2026-01-23T23:11:32.857431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0678
76.1%
1118
 
13.2%
280
 
9.0%
55
 
0.6%
35
 
0.6%
44
 
0.4%
61
 
0.1%
ValueCountFrequency (%)
0678
76.1%
1118
 
13.2%
280
 
9.0%
35
 
0.6%
44
 
0.4%
55
 
0.6%
61
 
0.1%
ValueCountFrequency (%)
61
 
0.1%
55
 
0.6%
44
 
0.4%
35
 
0.6%
280
 
9.0%
1118
 
13.2%
0678
76.1%

Ticket
Text

Distinct681
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2026-01-23T23:11:33.102221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.7508418
Min length3

Characters and Unicode

Total characters6015
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique547 ?
Unique (%)61.4%

Sample

1st rowA/5 21171
2nd rowPC 17599
3rd rowSTON/O2. 3101282
4th row113803
5th row373450
ValueCountFrequency (%)
pc60
 
5.3%
c.a27
 
2.4%
a/517
 
1.5%
ca14
 
1.2%
ston/o12
 
1.1%
212
 
1.1%
w./c9
 
0.8%
sc/paris9
 
0.8%
soton/o.q8
 
0.7%
soton/oq7
 
0.6%
Other values (709)955
84.5%
2026-01-23T23:11:33.435176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3746
12.4%
1689
11.5%
2594
9.9%
7490
8.1%
4464
 
7.7%
6422
 
7.0%
0406
 
6.7%
5387
 
6.4%
9328
 
5.5%
8282
 
4.7%
Other values (25)1207
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)6015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3746
12.4%
1689
11.5%
2594
9.9%
7490
8.1%
4464
 
7.7%
6422
 
7.0%
0406
 
6.7%
5387
 
6.4%
9328
 
5.5%
8282
 
4.7%
Other values (25)1207
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3746
12.4%
1689
11.5%
2594
9.9%
7490
8.1%
4464
 
7.7%
6422
 
7.0%
0406
 
6.7%
5387
 
6.4%
9328
 
5.5%
8282
 
4.7%
Other values (25)1207
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3746
12.4%
1689
11.5%
2594
9.9%
7490
8.1%
4464
 
7.7%
6422
 
7.0%
0406
 
6.7%
5387
 
6.4%
9328
 
5.5%
8282
 
4.7%
Other values (25)1207
20.1%

Fare
Real number (ℝ)

High correlation  Zeros 

Distinct248
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.204208
Minimum0
Maximum512.3292
Zeros15
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-23T23:11:33.531742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.225
Q17.9104
median14.4542
Q331
95-th percentile112.07915
Maximum512.3292
Range512.3292
Interquartile range (IQR)23.0896

Descriptive statistics

Standard deviation49.693429
Coefficient of variation (CV)1.5430725
Kurtosis33.398141
Mean32.204208
Median Absolute Deviation (MAD)6.9042
Skewness4.7873165
Sum28693.949
Variance2469.4368
MonotonicityNot monotonic
2026-01-23T23:11:33.643289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.0543
 
4.8%
1342
 
4.7%
7.895838
 
4.3%
7.7534
 
3.8%
2631
 
3.5%
10.524
 
2.7%
7.92518
 
2.0%
7.77516
 
1.8%
7.229215
 
1.7%
26.5515
 
1.7%
Other values (238)615
69.0%
ValueCountFrequency (%)
015
1.7%
4.01251
 
0.1%
51
 
0.1%
6.23751
 
0.1%
6.43751
 
0.1%
6.451
 
0.1%
6.49582
 
0.2%
6.752
 
0.2%
6.85831
 
0.1%
6.951
 
0.1%
ValueCountFrequency (%)
512.32923
0.3%
2634
0.4%
262.3752
0.2%
247.52082
0.2%
227.5254
0.4%
221.77921
 
0.1%
211.51
 
0.1%
211.33753
0.3%
164.86672
0.2%
153.46253
0.3%

Cabin
Text

Distinct148
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2026-01-23T23:11:33.901142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length1
Mean length1.5925926
Min length1

Characters and Unicode

Total characters1419
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)11.3%

Sample

1st rowM
2nd rowC85
3rd rowM
4th rowC123
5th rowM
ValueCountFrequency (%)
m687
74.3%
g64
 
0.4%
c234
 
0.4%
c254
 
0.4%
c274
 
0.4%
b964
 
0.4%
b984
 
0.4%
f4
 
0.4%
d3
 
0.3%
f23
 
0.3%
Other values (152)204
 
22.1%
2026-01-23T23:11:34.242674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M687
48.4%
272
 
5.1%
C71
 
5.0%
B64
 
4.5%
161
 
4.3%
359
 
4.2%
651
 
3.6%
545
 
3.2%
437
 
2.6%
837
 
2.6%
Other values (10)235
 
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1419
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M687
48.4%
272
 
5.1%
C71
 
5.0%
B64
 
4.5%
161
 
4.3%
359
 
4.2%
651
 
3.6%
545
 
3.2%
437
 
2.6%
837
 
2.6%
Other values (10)235
 
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1419
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M687
48.4%
272
 
5.1%
C71
 
5.0%
B64
 
4.5%
161
 
4.3%
359
 
4.2%
651
 
3.6%
545
 
3.2%
437
 
2.6%
837
 
2.6%
Other values (10)235
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1419
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M687
48.4%
272
 
5.1%
C71
 
5.0%
B64
 
4.5%
161
 
4.3%
359
 
4.2%
651
 
3.6%
545
 
3.2%
437
 
2.6%
837
 
2.6%
Other values (10)235
 
16.6%

Embarked
Categorical

Distinct3
Distinct (%)0.3%
Missing2
Missing (%)0.2%
Memory size7.1 KiB
S
644 
C
168 
Q
77 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters889
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowC
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S644
72.3%
C168
 
18.9%
Q77
 
8.6%
(Missing)2
 
0.2%

Length

2026-01-23T23:11:34.333033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-23T23:11:34.398240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
s644
72.4%
c168
 
18.9%
q77
 
8.7%

Most occurring characters

ValueCountFrequency (%)
S644
72.4%
C168
 
18.9%
Q77
 
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)889
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S644
72.4%
C168
 
18.9%
Q77
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)889
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S644
72.4%
C168
 
18.9%
Q77
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)889
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S644
72.4%
C168
 
18.9%
Q77
 
8.7%

Family_size
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9046016
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-23T23:11:34.456441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6134585
Coefficient of variation (CV)0.84713704
Kurtosis9.159666
Mean1.9046016
Median Absolute Deviation (MAD)0
Skewness2.7274415
Sum1697
Variance2.6032485
MonotonicityNot monotonic
2026-01-23T23:11:34.527190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1537
60.3%
2161
 
18.1%
3102
 
11.4%
429
 
3.3%
622
 
2.5%
515
 
1.7%
712
 
1.3%
117
 
0.8%
86
 
0.7%
ValueCountFrequency (%)
1537
60.3%
2161
 
18.1%
3102
 
11.4%
429
 
3.3%
515
 
1.7%
622
 
2.5%
712
 
1.3%
86
 
0.7%
117
 
0.8%
ValueCountFrequency (%)
117
 
0.8%
86
 
0.7%
712
 
1.3%
622
 
2.5%
515
 
1.7%
429
 
3.3%
3102
 
11.4%
2161
 
18.1%
1537
60.3%

ind_fare
Real number (ℝ)

High correlation  Zeros 

Distinct289
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.916375
Minimum0
Maximum512.3292
Zeros15
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-23T23:11:34.632968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.215
Q17.25
median8.3
Q323.666667
95-th percentile61.8771
Maximum512.3292
Range512.3292
Interquartile range (IQR)16.416667

Descriptive statistics

Standard deviation35.841257
Coefficient of variation (CV)1.7995874
Kurtosis87.300444
Mean19.916375
Median Absolute Deviation (MAD)3.06875
Skewness7.7655949
Sum17745.49
Variance1284.5957
MonotonicityNot monotonic
2026-01-23T23:11:34.744321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1359
 
6.6%
8.0550
 
5.6%
7.7539
 
4.4%
7.895838
 
4.3%
10.528
 
3.1%
26.5523
 
2.6%
7.92516
 
1.8%
7.77515
 
1.7%
015
 
1.7%
2614
 
1.6%
Other values (279)594
66.7%
ValueCountFrequency (%)
015
1.7%
1.1321428571
 
0.1%
2.4097333332
 
0.2%
2.5833333331
 
0.1%
2.6180666671
 
0.1%
2.6416666672
 
0.2%
2.8751
 
0.1%
2.88751
 
0.1%
3.1251
 
0.1%
3.24791
 
0.1%
ValueCountFrequency (%)
512.32922
0.2%
256.16461
 
0.1%
227.5253
0.3%
221.77921
 
0.1%
211.33751
 
0.1%
153.46251
 
0.1%
151.551
 
0.1%
146.52081
 
0.1%
135.63333
0.3%
134.51
 
0.1%

Family_type
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Single
537 
Small
292 
Large
62 

Length

Max length6
Median length6
Mean length5.6026936
Min length5

Characters and Unicode

Total characters4992
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSmall
2nd rowSmall
3rd rowSingle
4th rowSmall
5th rowSingle

Common Values

ValueCountFrequency (%)
Single537
60.3%
Small292
32.8%
Large62
 
7.0%

Length

2026-01-23T23:11:34.844335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-23T23:11:34.905729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
single537
60.3%
small292
32.8%
large62
 
7.0%

Most occurring characters

ValueCountFrequency (%)
l1121
22.5%
S829
16.6%
e599
12.0%
g599
12.0%
i537
10.8%
n537
10.8%
a354
 
7.1%
m292
 
5.8%
L62
 
1.2%
r62
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)4992
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l1121
22.5%
S829
16.6%
e599
12.0%
g599
12.0%
i537
10.8%
n537
10.8%
a354
 
7.1%
m292
 
5.8%
L62
 
1.2%
r62
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4992
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l1121
22.5%
S829
16.6%
e599
12.0%
g599
12.0%
i537
10.8%
n537
10.8%
a354
 
7.1%
m292
 
5.8%
L62
 
1.2%
r62
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4992
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l1121
22.5%
S829
16.6%
e599
12.0%
g599
12.0%
i537
10.8%
n537
10.8%
a354
 
7.1%
m292
 
5.8%
L62
 
1.2%
r62
 
1.2%

surname
Text

Distinct667
Distinct (%)74.9%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2026-01-23T23:11:35.127460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length16
Mean length6.8451178
Min length3

Characters and Unicode

Total characters6099
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique534 ?
Unique (%)59.9%

Sample

1st rowBraund
2nd rowCumings
3rd rowHeikkinen
4th rowFutrelle
5th rowAllen
ValueCountFrequency (%)
andersson9
 
1.0%
sage7
 
0.8%
johnson6
 
0.7%
skoog6
 
0.7%
van6
 
0.7%
goodwin6
 
0.7%
panula6
 
0.7%
carter6
 
0.7%
rice5
 
0.5%
ford4
 
0.4%
Other values (670)859
93.4%
2026-01-23T23:11:35.511050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e593
 
9.7%
n537
 
8.8%
a529
 
8.7%
o443
 
7.3%
r424
 
7.0%
l366
 
6.0%
s336
 
5.5%
i318
 
5.2%
t216
 
3.5%
d163
 
2.7%
Other values (44)2174
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)6099
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e593
 
9.7%
n537
 
8.8%
a529
 
8.7%
o443
 
7.3%
r424
 
7.0%
l366
 
6.0%
s336
 
5.5%
i318
 
5.2%
t216
 
3.5%
d163
 
2.7%
Other values (44)2174
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6099
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e593
 
9.7%
n537
 
8.8%
a529
 
8.7%
o443
 
7.3%
r424
 
7.0%
l366
 
6.0%
s336
 
5.5%
i318
 
5.2%
t216
 
3.5%
d163
 
2.7%
Other values (44)2174
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6099
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e593
 
9.7%
n537
 
8.8%
a529
 
8.7%
o443
 
7.3%
r424
 
7.0%
l366
 
6.0%
s336
 
5.5%
i318
 
5.2%
t216
 
3.5%
d163
 
2.7%
Other values (44)2174
35.6%

title
Categorical

High correlation  Imbalance 

Distinct13
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
mr
517 
miss
184 
mrs
125 
master
 
40
dr
 
7
Other values (8)
 
18

Length

Max length12
Median length2
Mean length2.7867565
Min length2

Characters and Unicode

Total characters2483
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.4%

Sample

1st rowmr
2nd rowmrs
3rd rowmiss
4th rowmrs
5th rowmr

Common Values

ValueCountFrequency (%)
mr517
58.0%
miss184
 
20.7%
mrs125
 
14.0%
master40
 
4.5%
dr7
 
0.8%
rev6
 
0.7%
royal3
 
0.3%
officer3
 
0.3%
major2
 
0.2%
ms1
 
0.1%
Other values (3)3
 
0.3%

Length

2026-01-23T23:11:35.604036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mr517
58.0%
miss184
 
20.6%
mrs125
 
14.0%
master40
 
4.5%
dr7
 
0.8%
rev6
 
0.7%
royal3
 
0.3%
officer3
 
0.3%
major2
 
0.2%
ms1
 
0.1%
Other values (4)4
 
0.4%

Most occurring characters

ValueCountFrequency (%)
m871
35.1%
r704
28.4%
s537
21.6%
i188
 
7.6%
e52
 
2.1%
a45
 
1.8%
t42
 
1.7%
o9
 
0.4%
d7
 
0.3%
v6
 
0.2%
Other values (9)22
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)2483
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m871
35.1%
r704
28.4%
s537
21.6%
i188
 
7.6%
e52
 
2.1%
a45
 
1.8%
t42
 
1.7%
o9
 
0.4%
d7
 
0.3%
v6
 
0.2%
Other values (9)22
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2483
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m871
35.1%
r704
28.4%
s537
21.6%
i188
 
7.6%
e52
 
2.1%
a45
 
1.8%
t42
 
1.7%
o9
 
0.4%
d7
 
0.3%
v6
 
0.2%
Other values (9)22
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2483
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m871
35.1%
r704
28.4%
s537
21.6%
i188
 
7.6%
e52
 
2.1%
a45
 
1.8%
t42
 
1.7%
o9
 
0.4%
d7
 
0.3%
v6
 
0.2%
Other values (9)22
 
0.9%

deck
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
M
687 
C
 
59
B
 
47
D
 
33
E
 
32
Other values (4)
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowM
2nd rowC
3rd rowM
4th rowC
5th rowM

Common Values

ValueCountFrequency (%)
M687
77.1%
C59
 
6.6%
B47
 
5.3%
D33
 
3.7%
E32
 
3.6%
A15
 
1.7%
F13
 
1.5%
G4
 
0.4%
T1
 
0.1%

Length

2026-01-23T23:11:35.710581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-23T23:11:35.803358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m687
77.1%
c59
 
6.6%
b47
 
5.3%
d33
 
3.7%
e32
 
3.6%
a15
 
1.7%
f13
 
1.5%
g4
 
0.4%
t1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
M687
77.1%
C59
 
6.6%
B47
 
5.3%
D33
 
3.7%
E32
 
3.6%
A15
 
1.7%
F13
 
1.5%
G4
 
0.4%
T1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M687
77.1%
C59
 
6.6%
B47
 
5.3%
D33
 
3.7%
E32
 
3.6%
A15
 
1.7%
F13
 
1.5%
G4
 
0.4%
T1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M687
77.1%
C59
 
6.6%
B47
 
5.3%
D33
 
3.7%
E32
 
3.6%
A15
 
1.7%
F13
 
1.5%
G4
 
0.4%
T1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M687
77.1%
C59
 
6.6%
B47
 
5.3%
D33
 
3.7%
E32
 
3.6%
A15
 
1.7%
F13
 
1.5%
G4
 
0.4%
T1
 
0.1%

Interactions

2026-01-23T23:11:29.348878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:24.922835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:26.023025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:26.822187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:27.463544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:28.121164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:28.742691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:29.438490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:25.101861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:26.216577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:26.910986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:27.552480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:28.207470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:28.825769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:29.718766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:25.264445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:26.357304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:27.005914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:27.643054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:28.303898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:28.923792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:29.808719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:25.406736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:26.450606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:27.099078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:27.770082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:28.404379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:29.008142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:29.901088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-23T23:11:27.187088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:27.867561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:28.491709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:29.092436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:29.988029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:25.674152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:26.633086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:27.283672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:27.953896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:28.573697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:29.178841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:30.073463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:25.842125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:26.727780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:27.373816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:28.034718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:28.659233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-23T23:11:29.261090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-23T23:11:35.890592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeEmbarkedFamily_sizeFamily_typeFareParchPassengerIdPclassSexSibSpSurviveddeckind_faretitle
Age1.0000.065-0.2280.3130.135-0.2540.0410.2690.099-0.1820.1550.1350.3660.259
Embarked0.0651.0000.0830.1280.1960.0520.0000.2600.1130.0920.1660.1820.1700.133
Family_size-0.2280.0831.0000.8160.5290.801-0.0500.1370.2050.8490.2150.012-0.2000.169
Family_type0.3130.1280.8161.0000.3070.6070.0350.1850.3000.8110.2850.1580.0000.396
Fare0.1350.1960.5290.3071.0000.410-0.0140.4790.1890.4470.2830.2870.6530.047
Parch-0.2540.0520.8010.6070.4101.0000.0010.0220.2470.4500.1570.025-0.2290.200
PassengerId0.0410.000-0.0500.035-0.0140.0011.0000.0320.066-0.0610.1040.0000.0200.034
Pclass0.2690.2600.1370.1850.4790.0220.0321.0000.1300.1480.3370.5980.2820.222
Sex0.0990.1130.2050.3000.1890.2470.0660.1301.0000.2060.5400.1820.1570.990
SibSp-0.1820.0920.8490.8110.4470.450-0.0610.1480.2061.0000.1870.017-0.1740.229
Survived0.1550.1660.2150.2850.2830.1570.1040.3370.5400.1871.0000.3200.1990.565
deck0.1350.1820.0120.1580.2870.0250.0000.5980.1820.0170.3201.0000.1910.129
ind_fare0.3660.170-0.2000.0000.653-0.2290.0200.2820.157-0.1740.1990.1911.0000.086
title0.2590.1330.1690.3960.0470.2000.0340.2220.9900.2290.5650.1290.0861.000

Missing values

2026-01-23T23:11:30.222010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-23T23:11:30.370559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-23T23:11:30.510571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedFamily_sizeind_fareFamily_typesurnametitledeck
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500MS23.62500SmallBraundmrM
1211Cumings, Mrs. John Bradley (Florence Briggs Thayer)female38.010PC 1759971.2833C85C235.64165SmallCumingsmrsC
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250MS17.92500SingleHeikkinenmissM
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S226.55000SmallFutrellemrsC
4503Allen, Mr. William Henrymale35.0003734508.0500MS18.05000SingleAllenmrM
5603Moran, Mr. JamesmaleNaN003308778.4583MQ18.45830SingleMoranmrM
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S151.86250SingleMcCarthymrE
7803Palsson, Master. Gosta Leonardmale2.03134990921.0750MS54.21500LargePalssonmasterM
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333MS33.71110SmallJohnsonmrsM
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708MC215.03540SmallNassermrsM
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedFamily_sizeind_fareFamily_typesurnametitledeck
88188203Markun, Mr. Johannmale33.0003492577.8958MS17.895800SingleMarkunmrM
88288303Dahlberg, Miss. Gerda Ulrikafemale22.000755210.5167MS110.516700SingleDahlbergmissM
88388402Banfield, Mr. Frederick Jamesmale28.000C.A./SOTON 3406810.5000MS110.500000SingleBanfieldmrM
88488503Sutehall, Mr. Henry Jrmale25.000SOTON/OQ 3920767.0500MS17.050000SingleSutehallmrM
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.1250MQ64.854167LargeRicemrsM
88688702Montvila, Rev. Juozasmale27.00021153613.0000MS113.000000SingleMontvilarevM
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S130.000000SingleGrahammissB
88888903Johnston, Miss. Catherine Helen "Carrie"femaleNaN12W./C. 660723.4500MS45.862500SmallJohnstonmissM
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C130.000000SingleBehrmrC
89089103Dooley, Mr. Patrickmale32.0003703767.7500MQ17.750000SingleDooleymrM